Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning A Performance Evaluation

This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Ital- ian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagno- sis of chronic pain and explore the potential of artificial intel- ligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms— XGBoost, gradient boosting, and BERT—were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agree- ment between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian lan- guage structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm se- lection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering in- sights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analy- sis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system.